{"id":"W4405349825","doi":"10.1371/journal.pone.0315939","title":"Trying to use temporal and kinematic parameters for the classification in wheelchair badminton","year":2025,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Spinal Cord Injury Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Interdisciplinary Research in Rehabilitation; Université du Québec à Montréal","funders":"Agence Nationale de la Recherche","keywords":"Wheelchair; Kinematics; Cluster analysis; Propulsion; Hierarchical clustering; Computer science; Engineering; Physical medicine and rehabilitation; Simulation; Artificial intelligence; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003327303,0.0009375303,0.0006251155,0.003282801,0.0007051189,0.002013784,0.0008194478,0.0008982624,0.002282517],"category_scores_gemma":[0.008574793,0.0002331476,0.0005493087,0.001918129,0.0007149261,0.001163173,0.001293832,0.0004689651,0.0008555611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005577445,"about_ca_system_score_gemma":0.0008705744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005744429,"about_ca_topic_score_gemma":0.01083642,"domain_scores_codex":[0.9985863,0.0003919224,0.000260728,0.0003271975,0.0002642504,0.000169685],"domain_scores_gemma":[0.9962074,0.001055034,0.001141692,0.0003005309,0.001111959,0.0001833753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004927786,0.0001031067,0.8908084,0.0006187918,0.000254403,0.0001815575,0.001401804,0.001236422,0.005437034,0.000344723,0.0005868565,0.09853411],"study_design_scores_gemma":[0.00001316159,0.0004470929,0.9790218,0.0006624628,0.0002199631,0.0005371914,0.005711795,0.006083569,0.002987935,0.0009156772,0.003326498,0.00007286698],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9767957,0.001205847,0.0180711,0.0003150204,0.0001278846,0.000134648,0.0006738269,0.0000799625,0.002596073],"genre_scores_gemma":[0.9893062,0.0003488876,0.009189272,0.00003862432,0.0000289296,0.00007507984,0.0004419718,0.00001562431,0.0005555042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005744429,"threshold_uncertainty_score":0.01759672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2473420924998487,"score_gpt":0.3911159525245577,"score_spread":0.143773860024709,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}